SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1926–1950 of 7282 papers

TitleStatusHype
RefiDiff: Refinement-Aware Diffusion for Efficient Missing Data Imputation—0
AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis—0
Communication-Efficient Diffusion Denoising Parallelization via Reuse-then-Predict Mechanism—0
Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction—0
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-RatesCode0
Restoration Score Distillation: From Corrupted Diffusion Pretraining to One-Step High-Quality Generation—0
Stochastic Orthogonal Regularization for deep projective priors—0
Anti-Inpainting: A Proactive Defense against Malicious Diffusion-based Inpainters under Unknown Conditions—0
RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers—0
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion modelCode0
Addressing Missing Data Issue for Diffusion-based RecommendationCode0
Few-Shot Concept Unlearning with Low Rank Adaptation—0
CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstruction—0
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
Multi-Stage Speaker Diarization for Noisy ClassroomsCode0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT AccelerationCode0
CRISP: Clustering Multi-Vector Representations for Denoising and Pruning—0
Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion—0
Noisemaker 3D: Comprehensive Framework for Mesh Noise GenerationCode0
A Fourier Space Perspective on Diffusion Models—0
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity—0
AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition—0
Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified